Abstract

In the context of random sampling, we show that linear full (separate) regression adjustment (FRA) on the control and treatment groups is, asymptotically, no less efficient than both the simple difference-in-means estimator and the pooled regression adjustment estimator; with heterogeneous treatment effects, FRA is usually strictly more efficient. We also propose a class of nonlinear regression adjustment estimators where consistency is ensured despite arbitrary misspecification of the conditional mean function. A simulation study confirms that nontrivial efficiency gains are possible with linear FRA, and that further gains are possible, even under severe mean misspecification, using nonlinear FRA.

摘要

在随机抽样的背景下，我们显示，对照组和治疗组的线性完全（单独）回归调整（FRA）在渐近性上不比简单均值差异估计器和合并回归调整估计器有效。具有异质的治疗效果，FRA通常严格更有效。我们还提出了一类非线性回归调整估计量，尽管条件均值函数有任意错误指定，但仍可确保一致性。仿真研究证实，使用线性FRA可以实现非凡的效率提升，并且使用非线性FRA即使在严重的均值错误的情况下也可以实现更高的效率。